An integrated energy-efficient train operation approach based on the space-time-speed network methodology
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DOI: 10.1016/j.tre.2021.102323
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Cited by:
- Yuan, Yin & Li, Shukai & Yang, Lixing & Gao, Ziyou, 2022. "Real-time optimization of train regulation and passenger flow control for urban rail transit network under frequent disturbances," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
- Huang, Yu & Zhou, Wenliang & Qin, Jin & Deng, Lianbo, 2023. "Optimization of energy-efficiency train schedule considering passenger demand and rolling stock circulation plan of subway line," Energy, Elsevier, vol. 275(C).
- Zhou, Wenliang & Huang, Yu & Deng, Lianbo & Qin, Jin, 2023. "Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail," Energy, Elsevier, vol. 263(PA).
- Shi, Jungang & Yang, Jing & Yang, Lixing & Tao, Lefeng & Qiang, Shengjie & Di, Zhen & Guo, Junhua, 2023. "Safety-oriented train timetabling and stop planning with time-varying and elastic demand on overcrowded commuter metro lines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
- Wang, Xuekai & D’Ariano, Andrea & Su, Shuai & Tang, Tao, 2023. "Cooperative train control during the power supply shortage in metro system: A multi-agent reinforcement learning approach," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 244-278.
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Keywords
Energy reduction; Regenerative energy; Space-Time-Speed network methodology; Dynamic programming; Discrete differential dynamic programming;All these keywords.
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